Summary: Fixes https://github.com/pytorch/pytorch/issues/134133
Test Plan:
Tested on the small repro in the linked issue with different lengths N (replacing 100), recording N vs. time taken in nanoseconds:
10 127268319
20 220839662
30 325463125
40 429259441
50 553136055
60 670799769
70 999170514
80 899014103
90 997168902
100 1168202035
110 1388556619
120 1457488235
130 1609816470
140 2177889877
150 1917560313
160 2121096113
170 2428502334
180 4117450755
190 4003068224
So N ~ 200 takes ~5s. Previously even smaller N would go for >1 min.
Didn't add a perf test because ezyang is planning to build a benchmark.
Also tested on https://www.internalfb.com/diff/D61560171, which now gets past the stuck point.
Differential Revision: D61619660
Pull Request resolved: https://github.com/pytorch/pytorch/pull/134150
Approved by: https://github.com/ezyang
Because aten.poisson doesn't have meta function registered, there is one additional eager execution of this op during compilation phase of torch.compile.
There are more ops without meta registration. Is there any reason for it?
Pull Request resolved: https://github.com/pytorch/pytorch/pull/134103
Approved by: https://github.com/ezyang
I had a night mare rewriting tests in test_misc.py specifically :
1. graphs can have comments that refers to my files "/lsakka/.." we really dont care about comments add option to ignore comments.
2. empty lines added when EXPECTTEST_ACCEPT=1 are changed with linter causing tests to fail or linter fail!
add flag to ignore empty lines.
3. EXPECTTEST_ACCEPT fails when the text have some not readable characters. those should not effect comparing strings, also those causes weird diffs comments when tests fails. I removed ansi_escape chars https://github.com/pytorch/pytorch/pull/133045
this is used in
Pull Request resolved: https://github.com/pytorch/pytorch/pull/134248
Approved by: https://github.com/aorenste
ghstack dependencies: #133639, #134364
This UT actual code only one empty line wrap difference(`linear` and `add`) between Windows/Linux, and the context is right.
Reproduce UTs:
```cmd
pytest test\dynamo\test_higher_order_ops.py -v -k test_functional_call_sequential_params_and_buffers
```
We can add `empty_line_normalizer` to fix it.
```cmd
______________________________________________________________________________________________ FuncTorchHigherOrderOpTests.test_functional_call_sequential_params_and_buffers _______________________________________________________________________________________________
Traceback (most recent call last):
File "D:\xu_git\dnnl_cb\pytorch\test\dynamo\test_higher_order_ops.py", line 3676, in test_functional_call_sequential_params_and_buffers
self.assertExpectedInline(
File "C:\Users\Xuhan\.conda\envs\win_mkl_static\lib\site-packages\torch\testing\_internal\common_utils.py", line 2871, in assertExpectedInline
return super().assertExpectedInline(actual if isinstance(actual, str) else str(actual), expect, skip + 1)
File "C:\Users\Xuhan\.conda\envs\win_mkl_static\lib\site-packages\expecttest\__init__.py", line 271, in assertExpectedInline
self.assertMultiLineEqualMaybeCppStack(expect, actual, msg=help_text)
File "C:\Users\Xuhan\.conda\envs\win_mkl_static\lib\site-packages\expecttest\__init__.py", line 292, in assertMultiLineEqualMaybeCppStack
self.assertMultiLineEqual(expect, actual, *args, **kwargs)
File "C:\Users\Xuhan\.conda\envs\win_mkl_static\lib\unittest\case.py", line 1226, in assertMultiLineEqual
self.fail(self._formatMessage(msg, standardMsg))
File "C:\Users\Xuhan\.conda\envs\win_mkl_static\lib\unittest\case.py", line 675, in fail
raise self.failureException(msg)
AssertionError: 'clas[509 chars]one\n add: "f32[1, 1]" = linear + l_buf[69 chars],)\n' != 'clas[509 chars]one\n\n add: "f32[1, 1]" = linear + l_b[71 chars],)\n'
class GraphModule(torch.nn.Module):
def forward(self, L_params_l1_weight_: "f32[1, 1]", L_params_l1_bias_: "f32[1]", L_buffers_buffer_: "f32[1]", L_inputs_: "f32[1, 1]"):
l_params_l1_weight_ = L_params_l1_weight_
l_params_l1_bias_ = L_params_l1_bias_
l_buffers_buffer_ = L_buffers_buffer_
l_inputs_ = L_inputs_
linear: "f32[1, 1]" = torch._C._nn.linear(l_inputs_, l_params_l1_weight_, l_params_l1_bias_); l_inputs_ = l_params_l1_weight_ = l_params_l1_bias_ = None
+ <<<< (difference is here )
add: "f32[1, 1]" = linear + l_buffers_buffer_; linear = l_buffers_buffer_ = None
return (add,)
: To accept the new output, re-run test with envvar EXPECTTEST_ACCEPT=1 (we recommend staging/committing your changes before doing this)
To execute this test, run the following from the base repo dir:
python test\dynamo\test_higher_order_ops.py FuncTorchHigherOrderOpTests.test_functional_call_sequential_params_and_buffers
This message can be suppressed by setting PYTORCH_PRINT_REPRO_ON_FAILURE=0
========================================================================================================================== short test summary info ==========================================================================================================================
FAILED [0.4275s] test/dynamo/test_higher_order_ops.py::FuncTorchHigherOrderOpTests::test_functional_call_sequential_params_and_buffers - AssertionError: 'clas[509 chars]one\n add: "f32[1, 1]" = linear + l_buf[69 chars],)\n' != 'clas[509 chars]one\n\n add: "f32[1, 1]" = linear + l_b[71 chars],)\n'
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/134394
Approved by: https://github.com/jansel
Co-authored-by: Jason Ansel <jansel@jansel.net>
After this I think all `using namespace` will have been eliminated from PyTorch header files. Internally, `-Wheader-hygiene` will prevent more from being added.
Test Plan: Sandcastle
Differential Revision: D61679037
Pull Request resolved: https://github.com/pytorch/pytorch/pull/134336
Approved by: https://github.com/Skylion007
Summary:
This enables patching extern modules to provide compatibility with serialized code depending on different versions of those extern modules.
The main motivation is to enable Numpy upgrade. In the recent release many alias to builtin types were deprecated and removed [1]. This breaks loading pickled modules that reference the removed aliases. While the proper solution is to re-generate pickled modules, it's not always feasible.
This proposes a way to define mapping with a new type, for a module member. It is only set if it's not present in the loaded module, thus removes the need to check for exact versions.
https://numpy.org/doc/stable/release/1.20.0-notes.html#using-the-aliases-of-builtin-types-like-np-int-is-deprecated
Differential Revision: D61556888
Pull Request resolved: https://github.com/pytorch/pytorch/pull/134376
Approved by: https://github.com/SherlockNoMad
If a mesh_dim_name is given, we will use the given mesh_dim_name to name the new flattened dim.
Otherwise, the default is a string concatentaing the mesh_dim_names of the given submesh with each mesh_dim_name separated by "_".
For example, if we have a 3D mesh DeviceMesh([[[0, 1], [2, 3]], [[4, 5], [6, 7]]], mesh_dim_names=("dp", "cp", "tp")), calling mesh_3d["dp", "cp"]._flatten() will create a 1D submesh DeviceMesh([0, 1, 2, 3], mesh_dim_names=("dp_cp",)) on rank 0, 1, 2, 3 and a 1D submesh DeviceMesh([4, 5, 6, 7], mesh_dim_names=("dp_cp",)) on rank 4, 5, 6, 7.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/134048
Approved by: https://github.com/fegin
ghstack dependencies: #133838, #133839
Sympy's implementation of Min/Max displays asymptotically bad behavior on `TORCH_COMPILE_CPROFILE=1 python torchrec/distributed/tests/test_pt2_multiprocess.py TestPt2Train.test_compile_multiprocess`. Evidence profile:
On this test case, we spend 42% of all time compiling the network on ShapeEnv.replace, which in turn spends all of its time in xreplace.
The problem appears to be find_localzeros call. By vendoring the implementations of Min/Max, we can potentially reduce the cost of this operation.
The implementation is copy-pasted sympy/functions/elementary/miscellaneous.py but with some adjustments:
* I deleted logic related to differentatiation, evalf and heaviside, as it's not relevant to PyTorch reasoning
* There's some massaging to appease PyTorch's linters, including a lot of noqa and type: ignore (which I could potentially refactor away with substantive changes, but that's better as its own change)
* I deleted the second loop iteration for is_connected, as an attempt at initial optimization (this also simplifies the port, since I can omit some code). I'll comment at that point what the exact difference is.
Before this change, the test in question takes 100s with 40 features; post this change, afterwards, it takes only 69s.
Signed-off-by: Edward Z. Yang <ezyang@meta.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/133319
Approved by: https://github.com/Skylion007
Summary:
Today there is no good mechanism to detect progress of non-strict export line-by-line in user code. This caused some pain recently in trying to find the exact line of user code that was triggering a bug where the process appeared stuck because deep down something was calling some symbolic shapes code that was suffering some exponential blowup.
This PR adds a environment variable for extended debugging that will log the line of user code corresponding to every torch function call. It only works in non-strict export for now. Prefix setting this environment variable with `TORCH_LOGS` enabled for `export` logs at `DEBUG` level (i.e., with a `+` prefix), i.e.,.:
```
TORCHEXPORT_EXTENDED_DEBUG_CURRENT_LOC=1 TORCH_LOGS="+export" ...
```
This will show logs with something like:
```
...
prim::device called at .../example.py:4284 in foo
TensorBase.item called at .../example.py:4277 in bar
...
```
We already have an existing place to intercept torch functions where we process data-dependent errors in non-strict, so parking the logging there. An alternative place we could be doing this is where we add `stack_trace` metadata when generating code, but unfortunately at least the example that motivated this gets stuck before generating code, so that would be too late.
Test Plan: ran it on some sample commands
Differential Revision: D61692156
Pull Request resolved: https://github.com/pytorch/pytorch/pull/134298
Approved by: https://github.com/angelayi
Summary: Create simple test that checks that FunctionEvent build tree happens lazily by checking that the metrics for it changes before and after call.
Test Plan: Make sure test passes in CI
Reviewed By: briancoutinho
Differential Revision: D61685429
Pull Request resolved: https://github.com/pytorch/pytorch/pull/134359
Approved by: https://github.com/briancoutinho
Fixes#133338
Test Plan:
```
TORCH_LOGS=dynamic python
import torch
torch._dynamo.config.capture_scalar_outputs = True
@torch.compile()
def f(x):
y = x.item()
torch._check_is_size(y)
r = torch.arange(y, dtype=torch.float32)
torch._check(r.size(0) == y)
return r
f(torch.tensor([300]))
```
Before and after diff. Verify the following line
```
I0813 11:05:44.890000 652898 torch/fx/experimental/symbolic_shapes.py:5198] [0/0] runtime_assert Eq(CeilToInt(IntTrueDiv(u0, 1)), u0) [guard added] at aa.py:10 in f (_dynamo/utils.py:2092 in run_node), for more info run with TORCHDYNAMO_EXTENDED_DEBUG_GUARD_ADDED="Eq(CeilToInt(IntTrueDiv(u0, 1)), u0)"
```
no longer shows in the logs. Also verify CI passes.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/134296
Approved by: https://github.com/aorenste
Current temporary directory path is hard code. Fixed by get temporary directory path by API.
Reproduce UTs:
```cmd
python test/dynamo/test_dynamic_shapes.py -v -k test_torch_package_working_with_trace_dynamic_shapes
```
Error message:
```cmd
________________________________________________________________________________________________ DynamicShapesMiscTests.test_torch_package_working_with_trace_dynamic_shapes ________________________________________________________________________________________________
Traceback (most recent call last):
File "D:\xu_git\dnnl_cb\pytorch\test\dynamo\test_misc.py", line 7199, in test_torch_package_working_with_trace
with package.PackageExporter(path) as exp:
File "C:\Users\Xuhan\.conda\envs\win_mkl_static\lib\site-packages\torch\package\package_exporter.py", line 237, in __init__
self.zip_file = torch._C.PyTorchFileWriter(f)
RuntimeError: Parent directory /tmp does not exist.
To execute this test, run the following from the base repo dir:
python test\dynamo\test_dynamic_shapes.py DynamicShapesMiscTests.test_torch_package_working_with_trace_dynamic_shapes
This message can be suppressed by setting PYTORCH_PRINT_REPRO_ON_FAILURE=0
========================================================================================================================== short test summary info ==========================================================================================================================
FAILED [0.0080s] test/dynamo/test_dynamic_shapes.py::DynamicShapesMiscTests::test_torch_package_working_with_trace_dynamic_shapes - RuntimeError: Parent directory /tmp does not exist.
==================================================================================================================== 1 failed, 1665 deselected in 4.00s =====================================================================================================================
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/134397
Approved by: https://github.com/ezyang
Fixes#130394
TorchInductor doesn't respect original strides of outputs. It opens up optimization opportunities like changing up memory layout. But for some cases, such as the case in https://github.com/pytorch/pytorch/issues/130394, we do need the output match the exact stride as required. The correctness is the first priority goal. So, this PR adds a new API `ir.ExternKernel.require_exact_strides(x, exact_strides, allow_padding=False)` to fix the issue. This PR enables non-dense outputs' strides follow the strides required by semantics.
The comparison between the original and after this fix for the test is the below.
```python
@triton.jit
def triton_(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
xnumel = 128
xoffset = tl.program_id(0) * XBLOCK
xindex = xoffset + tl.arange(0, XBLOCK)[:]
xmask = xindex < xnumel
x0 = xindex % 8
x1 = (xindex // 8)
- x2 = xindex
tmp0 = tl.load(in_ptr0 + (x0 + (16*x1)), xmask)
tmp1 = tmp0 + tmp0
- tl.store(out_ptr0 + (x2), tmp1, xmask)
+ tl.store(out_ptr0 + (x0 + (16*x1)), tmp1, xmask)
def call(args):
arg0_1, = args
args.clear()
assert_size_stride(arg0_1, (16, 8), (16, 1))
with torch.cuda._DeviceGuard(0):
torch.cuda.set_device(0)
- buf1 = empty_strided_cuda((16, 8), (8, 1), torch.float32)
+ buf1 = empty_strided_cuda((16, 8), (16, 1), torch.float32)
stream0 = get_raw_stream(0)
triton_poi_fused_add_copy_0.run(arg0_1, buf1, 128, grid=grid(128), stream=stream0)
del arg0_1
return (buf1, )
```
The buf1 is created with exact stride required by users, and its values are written in same stride with the input.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/130956
Approved by: https://github.com/eellison, https://github.com/blaine-rister
